The internet provides several sources of information which may be exploited. Internet news feeds and websites that allow users to interact with one another have exploded in popularity in the last few years. News feed channels such as CNN®, social networking websites sites such as Facebook® or LinkedIn®, and microblogging websites such as Twitter® enjoy widespread use. Millions of users post messages, images and videos on such websites on a daily, even hourly basis. Often, information gathered from these sources may refer to events taking place in real time. Such publicly accessible media may serve as a rich mine of information that may be used in different applications. For example, consider a scenario where a wide area emergency such as an earthquake or a flood has occurred and conventional emergency service lines are stressed beyond capacity; in this case users may turn to social media in order to request assistance. Another example of an event taking place in real time may be news feed reporting on civilians trapped under a building.
The high proliferation of information generated by media sources makes proper identification of events troublesome. Media data may contain ambiguous features which may hinder the ability of associating events with specific names, places or organizations. For example, a news feed may refer to a “Paris kidnapping”; however, in general, Paris may refer to a city in France, a city in Texas, or it may even refer to a person.
Thus a need exists for a method of detecting, extracting and validating events from media sources and effectively associate them with independent entities.